{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 2  # Choices: [1]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','250']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chatGPT_hatespeech_ngo'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_105836-gedg67p4</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4' target=\"_blank\">finetune_chetGPT_hatespeech_alliance</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x236ef28c250>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt models\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_2__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_2__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_2__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_2__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 880.225888324873, 869.5\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.629441624365482, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346809 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1734045 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 879.42, 868.0\n",
      "p5 / p95: 853.9, 918.2\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.18, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87942 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439710 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434751\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-8bX65Nk2pxurzGbeN2xwqfQV\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-2-t-1-trn-100:B5rbZVzz has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.11, validation loss=0.10, full validation loss=0.09\n",
      "Step 11/25: training loss=0.09, validation loss=0.06\n",
      "Step 12/25: training loss=0.10, validation loss=0.09\n",
      "Step 13/25: training loss=0.10, validation loss=0.09\n",
      "Step 14/25: training loss=0.09, validation loss=0.08\n",
      "Step 15/25: training loss=0.09, validation loss=0.13, full validation loss=0.09\n",
      "Step 16/25: training loss=0.12, validation loss=0.09\n",
      "Step 17/25: training loss=0.11, validation loss=0.05\n",
      "Step 18/25: training loss=0.09, validation loss=0.05\n",
      "Step 19/25: training loss=0.06, validation loss=0.07\n",
      "Step 20/25: training loss=0.05, validation loss=0.10, full validation loss=0.08\n",
      "Step 21/25: training loss=0.06, validation loss=0.08\n",
      "Step 22/25: training loss=0.07, validation loss=0.08\n",
      "Step 23/25: training loss=0.07, validation loss=0.04\n",
      "Step 24/25: training loss=0.04, validation loss=0.08\n",
      "Step 25/25: training loss=0.06, validation loss=0.09, full validation loss=0.08\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:55<00:00,  1.67it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.46575342465753417,\n",
      "                 'precision': 0.3953488372093023,\n",
      "                 'recall': 0.5666666666666667,\n",
      "                 'support': 60},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.847863247863248,\n",
      "                       'precision': 0.8953068592057761,\n",
      "                       'recall': 0.8051948051948052,\n",
      "                       'support': 308},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.45614035087719296,\n",
      "                  'precision': 0.41935483870967744,\n",
      "                  'recall': 0.5,\n",
      "                  'support': 26},\n",
      " 'accuracy': 0.748730964467005,\n",
      " 'macro avg': {'f1-score': 0.5899190077993249,\n",
      "               'precision': 0.5700035117082519,\n",
      "               'recall': 0.6239538239538239,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.7638242003658362,\n",
      "                  'precision': 0.7877631184629157,\n",
      "                  'recall': 0.748730964467005,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0WGbOyQLIriDMc7ZESaQLhGYSoESAxc/5sJp1FyWfekJoqz2vT3ShCOLrJSie6qqjMaotTjaFsBQLWpm3rAIbuNpWPhfn5zc2wNnmISxEAuKOBqszggBwLdsR7yf3wOUsZ9hJijGi/V/48pXvMXDjIKTmOtbES6HHyJxKYGEpICdLc6+z7CwrOLno/uPWyaUY2VmaPw9zsiSwtBIgcyxBdqY1Du7xgNy5BJ9uOAERAEsrAbs2N8C3jwWUvs0e4LOvTkAiUeFhgQU+imyDtKvM5j1OZYSh1+puuFxbGOhVwa5du2Bvb69RplRq/yLevHkzmjVrph7aHDJkCBISEgwO9Bo1aqTxuGHDhrh48aLOupMnT0ZKSgratGkDHx8fdO7cGeHh4Rg+fHiFu3XfvHkTn376KRo1aoSAgAB1+axZs/D+++9r1N21axe6deumfhwaGgqxWPOLPTc3FxYWFrh8+bJG3cro+54+KjY2Fh9++KFe7dcrj48GCBVXFbytIHj/8wuitKUUlvdKYbElF6U6Aj2LPXlQ+VpBCKx853aqHY9/tCIAwhOGg17bMgC2ViVo63kX74QeQWquHD9eKhvy+98dT/zvzj8ZnpO3vfDt0G8xvN0ZxB58xsi9p8pofbYiQKhsO47HPneRSNBoqE1wNgaPu4G4+QG4+JcMXt4PMeHdy8jOvI5Nq3zV5928Zou3XgmGvUMpwnrdw7T55zHz9Q4M9h6jEsRQVXPVbXXPry0M9Kqge/fuiI+P1yg7evQoRowYoVGWkJCgUTZixAh07doVOTk5eg/FAkBKSorG3DhLy4o/Njs7O/zwww+4cuUK9u3bhyNHjmDatGlYtmwZfv/9d9jalmUQcnNzYW9vD0EQUFBQgKeeegrbtm3T2HF7xowZGD16tEb7DRtqrgJLSkpCixYtNMrK5/jpe1sXQP/39FGzZ8/G1KlT1Y8VCgW8vb31ut6/kswCghgQ3Vdq/pLIUZZl9fQktLCG+Bcdk68LVRDvz4fytYrn+lHtyH4oRalKpJW9c7Z9iKyHFQ+7A8AtRdl83ctZLnCxfYiITn+qA73HCRDhzF13NHbMMUq/6ckU2VZQloq0sneOziXIydI9ipGdJYGTq2Z9uXMJSktEUOSWnTPyrWv4daeHet7f9cv2kNoqMXnORWxe7aP+A6G0VIz0tLKf85fPydCsdR76j7iJ/0Y3N+rrpPqDgV4V2NnZoWnTphplN2/e1Hh87tw5HD16FH/++SdmzZqlLlcqldi0aRPefPNNva/n5+dnUGAIAE2aNEGTJk0wduxYREVFISAgAElJSXj99dcBlM2/O3HiBMRiMTw8PGBnp/3Xnqurq9brfJy3t3eFdQICAnD+/Hm9+qvPe/o4a2vrJ95TsF6xEkFoJoH4RCFUz/zzeYhPFEIVov/ketHfxYCzdmAoPpgPlAhQPse/7OtaqcoC5zLcENL4Jn656q8uD2l8E/uu+urdjggCJBaqSmoICHTLxOVMbq9RW0pLxfj7nD06hNzH77/+MzeyQ8h9HNmne8rE+dNydHo2U6PsqdD7uHzOAcrSsqyRtY1SK9urUoogEv1/trCCzL8IAqwklX2NmCclRFBWc8Pk6p5fW+pH3rEeSkhIQNeuXXH69GmcOnVKfcycORMJCQm12hdfX1/Y2toiP/+fLI9YLEbTpk3h7++vM8gzhmHDhuHnn3/GyZMntZ4rLS3V6A+VUQ6UQ7wnD+I9eRClFsMi/j5EGaVQvliW0bVIyIblwnvq+hbbciH+LR+iWyUQXS+GRUI2LA4VQNnfQattiz0PoAq11VqFS3Vjw8l2GNjqPF5ueR7+TtmY2eU3eNnnIemvsqkekaFHENPrn9XyQ9qewbN+19FYnoPG8hwMaHEBo586jV0X/snmvfn0nwhtnIpGMgWau2bio+f2o7lrFpLOtHz88lSDvtvgjd4D09FrQDq8/fIxbuZluHkVYfc3ZSMio9++gmkf/7Mtzu5vGsDdqxDjZlyGt18+eg1IR/h/0rEt8Z8Rij/2u+KFV2+ha5+78Gj4EB1C7mPkW9dwdL8rVKqygOO1KVfQ6qkcuDd4CN9mDzBq8lW06ZiD/T9wv8zHlQ/dVveoD5jRqwElJSX48ssvER0drbUgYuzYsVi4cCFOnz6Ndu3aVdCCpoyMDBQWau6/5OLionMxw7x581BQUIDnn38ePj4+yMnJwfLly1FSUoJevXoZ9Dry8vK09uuztbXV2OolKytLq46joyOkUikiIyPxww8/4LnnnsNHH32EZ555Bg4ODjh27BgWLFiAhIQEbq/yGFU3O5QqlLD8Oge4r4TgI0HJfA/Ao+xbVXS/FKJH99QrBSzXZAOZSsBaBMHHCiXz3aF6WjMDKLpZAvGZIhTH8gf+v8Wey00hlxZi4tPH4WaXj8tZznhzxwtIzysL0l1tC+Dl8EBdXwwBkaFH0FCWB6VKjLRcGZYe7oRv/vpneyMH62LM63EArnYFyCuS4MI9N4ze2h9n7vJzr00H93rAwbEUwyZeh7NbEa7/bYe5EW2RkV42b9bJrRhuXv8sJLt7ywZzJrXF+Bl/48Uht5CVYY1Vsc3UW6sAwKbVPhAEYNTka3BxL0JuthX+OOCK9cv91HUcXUowPeY8nN2KkJ9niWuX7THnzXY4+bvmil4yLwz0asCOHTuQlZWFl19+Weu5Zs2aoU2bNkhISMDy5cv1aq98u5NH/f777+jcWXuvrWeffRaff/45Ro0ahbt378LJyQkdOnTATz/9pLOdysyZMwdz5szRKJswYQJWrlypftyzZ0+t8zZt2oQhQ4bA2toaycnJWLJkCVatWoXp06fD1tYWLVq0wJQpU6p0zz5zoOonQ3E/mc7nSmdobpOhfFUO5atP3jpBaGSFop98jdE9MqKkv1oj6S/d3wfv/9xD4/HG/7XBxv+1qbS9hSlhWJgSZrT+UdX9kNQQPyQ11PnckvdbaJWdOeaEKYM7VtieSinGxpV+2LjSr8I6y+YGGt5RM6VE9YdeK18u+O8hEoSKRvaJ6heFQgG5XI6Q7yfD0s6E5u6RTpm/6r5VGJmmxmsu1HUXqBaUqorxy/1E5Obmat0owBjKf0+8fyQcUvvqbfFV+KAE8zv/VGN9NRZm9IiIiMisKAUxlNWcY1fd82tL/eglERERERmMGT0iIiIyKwJEUFVzjl6lG2D/izDQIyIiIrPCoVsiIiIiqveY0SMiIiKzohJEUD3hvtL6tFEfMNAjIiIis6KEGMpqDmpW9/zaUj96SUREREQGY0aPiIiIzAqHbomIiIhMlApiqKo5qFnd82tL/eglERERERmMGT0iIiIyK0pBBGU1h16re35tYaBHREREZoVz9IiIiIhMlCCIoarmnS0E3hmDiIiIiOoSM3pERERkVpQQQYlqztGr5vm1hYEeERERmRWVUP05dirBSJ2pYRy6JSIiIjJRzOgRERGRWVEZYTFGdc+vLQz0iIiIyKyoIIKqmnPsqnt+bakf4SgRERERGYwZPSIiIjIrvDMGERERkYkypzl69aOXRERERGQwZvSIiIjIrKhghHvd1pPFGAz0iIiIyKwIRlh1KzDQIyIiIvr3UQlGyOjVk8UYnKNHREREVEvi4uLg5+cHqVSKoKAgpKSkVFq/qKgIUVFR8PHxgbW1NZo0aYK1a9fqfT1m9IiIiMis1NWq26SkJERGRiIuLg5hYWFYtWoV+vbti3PnzqFx48Y6z3n11Vdx9+5dJCQkoGnTpsjIyEBpaane12SgR0RERGalroZuFy9ejDFjxmDs2LEAgKVLl2Lv3r2Ij49HbGysVv09e/bgwIEDuHr1KpydnQEAvr6+Bl2TQ7dEREREVaRQKDSOoqIinfWKi4tx/PhxhIeHa5SHh4fj8OHDOs/ZsWMHgoODsXDhQjRs2BABAQGYPn06Hj58qHf/mNEjIiIis2LMe916e3trlM+dOxfz5s3Tqp+ZmQmlUgkPDw+Ncg8PD9y5c0fnNa5evYpDhw5BKpXiu+++Q2ZmJiIiInD//n295+kx0CMiIiKzYsyh27S0NMhkMnW5tbV1peeJRJrXFQRBq0x9DZUKIpEIX3/9NeRyOYCy4d9XXnkFn3/+OWxsbJ7YTw7dEhEREVWRTCbTOCoK9FxdXWFhYaGVvcvIyNDK8pXz8vJCw4YN1UEeALRo0QKCIODmzZt69Y+BHhEREZmV8oxedQ9DSCQSBAUFITk5WaM8OTkZoaGhOs8JCwvD7du38eDBA3XZpUuXIBaL0ahRI72uy0CPiIiIzEpdBHoAMHXqVHzxxRdYu3Ytzp8/j3feeQepqamYOHEiAGD27NkYNWqUuv6wYcPg4uKC119/HefOncPBgwcxY8YMvPHGG3oN2wKco0dERERUKwYPHoysrCxER0cjPT0drVu3xu7du+Hj4wMASE9PR2pqqrq+vb09kpOTMXnyZAQHB8PFxQWvvvoq5s+fr/c1GegRERGRWanLW6BFREQgIiJC53OJiYlaZYGBgVrDvYZgoEdERERmRQCqvb2KYJyu1DgGekRERGRW6jKjV9u4GIOIiIjIRDGjR0RERGbFnDJ6DPSIiIjIrJhToMehWyIiIiITxYweERERmRVzyugx0CMiIiKzIggiCNUM1Kp7fm3h0C0RERGRiWJGj4iIiMyKCqJqb5hc3fNrCwM9IiIiMivmNEePQ7dEREREJooZPSIiIjIr5rQYg4EeERERmRVzGrploEdERERmxZwyepyjR0RERGSimNEjkyOdawdLC+u67gbVsDM/xNV1F6gW9f6kfV13gWqBUiiplesIRhi6rS8ZPQZ6REREZFYEAIJQ/TbqAw7dEhEREZkoZvSIiIjIrKgggoh3xiAiIiIyPVx1S0RERET1HjN6REREZFZUgggibphMREREZHoEwQirbuvJslsO3RIRERGZKGb0iIiIyKyY02IMBnpERERkVhjoEREREZkoc1qMwTl6RERERCaKGT0iIiIyK+a06paBHhEREZmVskCvunP0jNSZGsahWyIiIiITxYweERERmRWuuiUiIiIyUcL/H9Vtoz7g0C0RERGRiWJGj4iIiMwKh26JiIiITJUZjd1y6JaIiIjMy/9n9KpzoIoZvbi4OPj5+UEqlSIoKAgpKSkV1t2/fz9EIpHWceHCBb2vx0CPiIiIqBYkJSUhMjISUVFROHnyJLp06YK+ffsiNTW10vMuXryI9PR09dGsWTO9r8lAj4iIiMxK+Z0xqnsYavHixRgzZgzGjh2LFi1aYOnSpfD29kZ8fHyl57m7u8PT01N9WFhY6H1NBnpERERkVqo7bPvoYg6FQqFxFBUV6bxmcXExjh8/jvDwcI3y8PBwHD58uNL+dujQAV5eXnjuueewb98+g14rAz0iIiKiKvL29oZcLlcfsbGxOutlZmZCqVTCw8NDo9zDwwN37tzReY6XlxdWr16NrVu3Ytu2bWjevDmee+45HDx4UO/+cdUtERERmZdqLKbQaANAWloaZDKZutja2rrS00QizesKgqBVVq558+Zo3ry5+nFISAjS0tKwaNEidO3aVa9uMqNHREREZsWYc/RkMpnGUVGg5+rqCgsLC63sXUZGhlaWrzKdO3fG5cuX9a7PQI+IiIiohkkkEgQFBSE5OVmjPDk5GaGhoXq3c/LkSXh5eeldn0O3REREZF7qaMPkqVOnYuTIkQgODkZISAhWr16N1NRUTJw4EQAwe/Zs3Lp1Cxs2bAAALF26FL6+vmjVqhWKi4vx1VdfYevWrdi6dave12SgR0RERGalrm6BNnjwYGRlZSE6Ohrp6elo3bo1du/eDR8fHwBAenq6xp56xcXFmD59Om7dugUbGxu0atUKP/zwA55//nm9r6lXoLd8+XK9G5wyZYredYmIiIjMSUREBCIiInQ+l5iYqPF45syZmDlzZrWup1egt2TJEr0aE4lEDPSIiIjo36+e3Ku2uvQK9K5du1bT/SAiIiKqFXU1dFsXqrzqtri4GBcvXkRpaakx+0NERERUswQjHfWAwYFeQUEBxowZA1tbW7Rq1Uo9aXDKlCn45JNPjN5BIiIiIqoagwO92bNn4/Tp09i/fz+kUqm6vGfPnkhKSjJq54iIiIiMT2Sk49/P4O1Vvv/+eyQlJaFz584at+xo2bIlrly5YtTOERERERldHe2jVxcMzujdu3cP7u7uWuX5+fkV3quNiIiIiGqfwYFex44d8cMPP6gflwd3a9asQUhIiPF6RkRERFQTzGgxhsFDt7GxsejTpw/OnTuH0tJSLFu2DGfPnsXvv/+OAwcO1EQfiYiIiIxHEJUd1W2jHjA4oxcaGorffvsNBQUFaNKkCX766Sd4eHjg999/R1BQUE30kYiIiIiqoEr3um3Tpg3Wr19v7L4QERER1ThBKDuq20Z9UKVAT6lU4rvvvsP58+chEonQokUL9O/fH5aWVWqOiIiIqPaY0apbgyOzM2fOoH///rhz5w6aN28OALh06RLc3NywY8cOtGnTxuidJCIiIiLDGTxHb+zYsWjVqhVu3ryJEydO4MSJE0hLS0Pbtm0xfvz4mugjERERkfGUL8ao7lEPGJzRO336NI4dOwYnJyd1mZOTEz7++GN07NjRqJ0jIiIiMjaRUHZUt436wOCMXvPmzXH37l2t8oyMDDRt2tQonSIiIiKqMWa0j55egZ5CoVAfMTExmDJlCrZs2YKbN2/i5s2b2LJlCyIjI7FgwYKa7i8RERER6UmvoVtHR0eN25sJgoBXX31VXSb8/xrjl156CUqlsga6SURERGQkZrRhsl6B3r59+2q6H0RERES1g9uraHr22Wdruh9EREREZGRV3uG4oKAAqampKC4u1ihv27ZttTtFREREVGOY0avYvXv38Prrr+PHH3/U+Tzn6BEREdG/mhkFegZvrxIZGYns7GwcOXIENjY22LNnD9avX49mzZphx44dNdFHIiIiIqoCgzN6v/76K7Zv346OHTtCLBbDx8cHvXr1gkwmQ2xsLF544YWa6CcRERGRcZjRqluDM3r5+flwd3cHADg7O+PevXsAgDZt2uDEiRPG7R0RERGRkZXfGaO6R31gcEavefPmuHjxInx9fdG+fXusWrUKvr6+WLlyJby8vGqij0Rm5cUXLuGV/5yHs/ND3EiVY+XqIJw9666zrrPTQ4wbewLNmt5HgwZ52L6jOVatCdKo06vnVUx754jWuS8NGIySEosaeQ2kn52JLvg23h33M6zgE1CIidG30KZTfoX1f93mhG/i3HH7qjXsZEoEdVNg/JzbkDmXzY2+flGKDZ964u//2eLuTQkmfHgL/xl3r7ZeDj3ixdcyMejNe3B2L8GNS1KsnNMAZ/6wr7B+m84PMGHebfgEFCLrrhW+jXPDD1+66qz7bP9svBefisN7ZPjwDT91+eC37iLs+Vx4Ny1CcaEY547ZIuFjL9y8IjX666P6o0pz9NLT0wEAc+fOxZ49e9C4cWMsX74cMTExBrU1evRoDBgwQKNsy5YtkEqlWLhwIQBg3rx5EIlEWkdgYKD6nG7duiEyMlLjsUgkwubNmzXaXrp0KXx9fdWPExMTdbYtlVb8TbF//36IRCLk5ORoPefr64ulS5dqlcfExMDCwgKffPKJRl1d1y4/unXrVmm9R9t63NWrVzF06FA0aNAAUqkUjRo1Qv/+/XHp0iV1nUfbcnBwQHBwMLZt26Z+Xt/3XVediRMnavRn3759eP755+Hi4gJbW1u0bNkS06ZNw61bt6r8npqqrl1uYMK4E9ic1AqTpvTFmTPumP/hfri56f7lb2WlRG6uNTYltcLVa0466wBAfr4Vho54WeNgkFe39m93xMq5DTF0yl3E/XQRrTvl4/3h/si4aaWz/pmjdvh0SmP0GZKF1fsvIGrVdVw6bYsl073VdYoeiuDVuBhvvHcbzu4ltfVS6DHP9svGxA9vY9Nyd0SEB+DMUTvM//oa3BoW66zv4V2E+V9dw5mjdogID8DmFe5486PbeOb5HK267g2LMe6DdPx1xE7rubYh+diZ6IrIF5th9hB/WFgIiNl0FdY2XCSpxYxugWZwRm/48OHqf3fo0AHXr1/HhQsX0LhxY7i66v7rQ19ffPEFJk2ahM8//xxjx45Vl7dq1Qo///yzRl1Ly8q7LpVK8f7772PgwIGwstL9gxMAZDIZLl68qFH26F1AjGHdunWYOXMm1q5di3fffRcA8Oeff6pXKB8+fBgDBw7ExYsXIZPJAAASiUR9fnR0NMaNG6fRpoODg85rFRcXo1evXggMDMS2bdvg5eWFmzdvYvfu3cjNzdXqV58+fZCTk4NPP/0UgwYNwqFDhxASEgJAv/d93LhxiI6O1iiztbVV/3vVqlWIiIjAa6+9hq1bt8LX1xepqanYsGEDPvvsMyxevLjyN8/M/OflC9j7kz/2/FR23+hVa4IQFJSOF5+/jHXr22vVv5thj5WrgwEAvXtdrbBdQQCys21qpM9UNdtWu6H30PvoO/w+AODN6Fs4vt8Buza44o330rXqnz9hCw/vYgwYmwkA8GxcjBdGZOGbuH+yvc3bP0Tz9g8BAGtjGtTCqyBd/jM+E3s3OWPPRhcAwMq5DRHULQ8vjsrCuljtka8XR2Uh45YVVs5tCABI+1uKgLYPMXDiPRza7aiuJxYLmPX5DXz5mQdaP50Pe7lmABc13F/j8WfvNMY3Z86iWduHOHO04mwimbYq76NXztbWFk899VS1O7Jw4ULMmTMHGzduxMCBAzWes7S0hKenp0HtDR06FDt37sSaNWsQERFRYT2RSGRw24Y4cOAAHj58iOjoaGzYsAEHDx5E165d4ebmpq7j7OwMAHB3d4ejo6NWGw4ODnr38dy5c7h69Sp+/fVX+Pj4AAB8fHwQFhamVdfR0RGenp7w9PTEypUrsXnzZuzYsUMd6Onzvtva2lZY5+bNm5gyZQqmTJmCJUuWqMt9fX3RtWtXnRk8c2ZpqUSzpvfxzbctNcpPnPBEixaZ1WrbxqYU69d9D7FYwNWrTtjwZVtcuepcrTap6kqKRbj8P1sMfitDozzo2TycO6adqQGAlsH5WL/AC3/84oCOPfKQk2mJlB8c8XRPRW10mfRkaaVCs7YFSPqv5nSL4wcc0DJYd2a+RVABjh/Q/OP92H4H9B6aBQtLAcrSsuTD8Kl3kZtlib2bXND66YqH+MvZycoCwbwcZu8fJ0L159jVj6UYegZ6U6dO1bvBqmRo3n33XXz++efYtWsXevbsafD5ushkMrz33nuIjo7Ga6+9Bjs73T88a1pCQgKGDh0KKysrDB06FAkJCejatWuNXc/NzQ1isRhbtmxBZGQkLCz0+wa3srKCpaUlSkqMN9zz7bffori4GDNnztT5vK6g1hBFRUUoKipSP1Yo6vcvPJmsCBYWArJzNKcOZOfYwNlJO8Ojr7Q0GT5b0hnXrjvC1rYEA/pdxGefJiNicl/cvi2rbrepChT3LaBSiuDoqvn95uhWguwM3dn6Vh0LMOu/NxAz0RfFRWIoS0XoHJ6LSfNv1kaXSU8yZyUsLIGcTM1frzn3LOHkXqrzHCe3EuTc0/zcczItYWkFyJ1LcT/DCi075qP3kPuICA/QsycCxs+7jTNH7XDjIrP55kyvOXonT57U6zh16pTBHfjxxx+xYMECbN++vcIg76+//oK9vb3G8ejQbkUiIiIglUorDT5zc3O12g4PD39i240aNdI6LzU1VaOOQqHA1q1bMWLECADAiBEjsGXLFoMDklmzZmlda//+/TrrNmzYEMuXL8ecOXPg5OSEHj164KOPPsLVqxUP6xUVFWH+/PlQKBR47rnn1OX6vO9xcXFaddavXw8AuHz5MmQymd6LdPR5Tx8VGxsLuVyuPry9vSusW6889lemSCRAqMYy/gsXXfHrPj9cu+aEs2fdEfPJM7h12wH9X7r05JOpRj0+S0QQRBWmCW5cskbcB40w/J07+O+ei/h44xXcTZNg+SwT+bo3MYLW9zEqndP1eP3yrwNBAGzslJi1IhVLZzSC4r5+A3GTYm7Br8VDxEY01rvPZqV8e5XqHvWAXl8x+/btq7EOtG3bFpmZmZgzZw46duyoc+5Z8+bNtTZjrmiO2qOsra0RHR2Nt956C2+++abOOg4ODlrbwtjYPPmvn5SUFK0+lC+gKLdx40b4+/ujXbt2AID27dvD398fmzdvxvjx4594jXIzZszA6NGjNcoaNmxYYf1JkyZh1KhR2LdvH44ePYpvv/0WMTEx2LFjB3r16qWuN3ToUFhYWODhw4eQy+VYtGgR+vbtq35en/d9+PDhiIqK0igr335HEASD5jvq854+avbs2RrZZoVCUa+DPYXCGkqlCE5OhRrljvJCrSxfdQiCCJcuuaBBgzyjtUmGkTkrIbYQkH1Pc/5wbqYlnNx0Z32SVnigVcd8DIooW0Xr37IQUpubmPZyM7w2Kx0uHrrPo9qluG8BZSm0Pke5aymy7+n+lZt9z0or2+foUorSEkCRbQmf5oXwbFyM6PXX1M+L/j9Nszv1NMZ0CUT6DWv1cxHzbyIkXIFpLzdBZroEpIMZ3Rmj2nP0qqthw4bYunUrunfvjj59+mDPnj1av+wlEgmaNm1apfZHjBiBRYsWYf78+RorbsuJxeIqte3n56c19Pj4QoW1a9fi7NmzGuUqlQoJCQkGBXqurq4G99HBwQH9+vVDv379MH/+fPTu3Rvz58/XCPSWLFmCnj17QiaTqYOzR+nzvsvl8grrBAQEIDc3F+np6Xpl9fR5Tx9lbW0Na2vrCp+vb0pLLXD5b2d06HAHh3//J2Dt0OEOjhxpZMQrCWjin41r1x2N2CYZwkoioFnbApw46ICwvv8skjpx0AEhvXN1nlP4UAwLC83fLOLyx/XkF445KC0R4/L/bPFU1zwc3iNXlz/VNQ+/75XrPOf8cVt06qU50hP0bB4unbaFslSEtL+tMb675pDt6Fl3YGOnQvycBrh3u/wPBgGTPr6F0D65mPFKU9xNM52fj1R1Bm+vUhMaN26MAwcOICMjA+Hh4UadayUWixETE4P4+Hhcv37daO0+yV9//YVjx45h//79OHXqlPo4ePAg/vzzT5w5c6bW+lK+LUp+vubkXU9PTzRt2lRnkGcMr7zyCiQSiXqrnMdxMYa2bd8Fok/4FYT3ugJv71yMH3cc7m4F+GF3MwDA66+dwvSphzXO8ffPhr9/NqQ2pZDLC+Hvn43G3v8EC8OH/oWgp27D0/MB/P2z8c7bR+Hvn40ffqzaH09kHP8Zfw97Njpj7yZnpF62xsq5DZBxywovjCpbeLM2xgsLp/wz7Na5lwK//eiInetdkH5DgrN/2CH+g0Zo3iEfLp5l2aCSYhGunLHBlTM2KCkRISvdClfO2ODWNWZ1atO21a7oM+w+wodkwbtpISbMuwX3hiX4YUPZKtzXZ6djxrJ/pqXs2uACj0YlGD/3FrybFiJ8SBZ6D72PrSvLFu2VFIlx46KNxvEg1wIP88vKS0vKfpW/FXMLPf6TjU8m+eDhAzGc3Erg5FYCiVRV+2/Cvx23V6l9jRo1wv79+9G9e3eEh4dj7969kMvL/vopLS3FnTt3NOqLRCJ4eHjo1faLL76ITp06YdWqVVrnCIKg1TZQNvwoFlc9Dk5ISMDTTz+tc+FFSEgIEhISNFaiViYvL0+rj7a2tuqtWB516tQpzJ07FyNHjkTLli0hkUhw4MABrF27FrNmzTLoNejzvhcUFGjVsba2hpOTE7y9vbFkyRK89dZbUCgUGDVqFHx9fXHz5k1s2LAB9vb2+Oyzzwzqk6k7mOIDmawIw4eegZPzQ9y4IccHc7sh417ZYiJn54dwdyvQOCduxY/qfwc0u48e3W/g7l07vPZGfwCAnX0xpkz+A05OhSjIt8KVK06YMasnLl2q3nZIVD3d+ucgL9sCXy/xxP2MsuG5+V9dhUejsgUa9zOscO/WPwFa+OD7ePhAjB3rXLHmw4awkyvRPiwPY6L+WaiTddcKEeHN1Y+3rHTHlpXuaBvyAJ9u/bv2XpyZO7DDCQ5OSgx/5y6c3Utx46IU74/wQ8b/f57O7iUae+rdTbPG+yP8MOHD23hpdBbu37VC/AcNNLZW0cdLo7MAAIu2XdEoXxTpjeRvuMr+Uca4s4XJ3hmjJjVs2BAHDhxA9+7d0atXL/z0008AgLNnz2oN/VlbW6OwsFBXMzotWLAAoaGhWuUKhULnsGJ6enqVt10pLi7GV199VWFgNXDgQMTGxmLBggUa++VVZM6cOZgzZ45G2YQJE7By5Uqtuo0aNYKvry8+/PBDXL9+HSKRSP34nXfeMeh16PO+r1mzBmvWrNGo07t3b+zZswdA2YKYgIAALFq0CC+//DIePnwIX19fvPjiiwat5jYnu34IwK4fdK+s+2xJiFZZnxeGVdre6jVBWP3Y3TLo3+Gl0VnqX86Pm75UeyFS/zGZ6D+m4q12PL2Lsff2KWN1j6ph13pX7Fqv+4+pz97RXiDx1xF7vNVb3xW1utvo3aCd/h0ksyESBK21PkT1kkKhgFwuR/d278LSgnNTTN2eH76u6y5QLerdoH1dd4FqQalQgv3YjtzcXJ2jVtVV/nvCd/7HEFdyFyx9qAoLcf39qBrrq7FUaWzyyy+/RFhYGBo0aIAbN24AKLu92Pbt243aOSIiIiKjM6M5egYHevHx8Zg6dSqef/555OTkqG/j5ejoaFb3JCUiIiIyVFxcHPz8/CCVShEUFISUlBS9zvvtt99gaWmJ9u3bG3Q9gwO9FStWYM2aNYiKitK460JwcDD++usvQ5sjIiIiqlXlizGqexgqKSkJkZGRiIqKwsmTJ9GlSxf07du30psDAGU3dxg1apTGTQ30ZXCgd+3aNXTo0EGr3NraWmv7DiIiIqJ/HSPeGUOhUGgcj96a83GLFy/GmDFjMHbsWLRo0QJLly6Ft7c34uPjK+3uhAkTMGzYMPW96A1hcKDn5+en81ZnP/74I1q2bKl9AhEREdG/iRHn6Hl7e2vcjjM2NlbnJYuLi3H8+HGt26yGh4fj8OHDOs8BgHXr1uHKlSuYO3dulV6qwdurzJgxA5MmTUJhYSEEQcAff/yBTZs2ITY2Fl988UWVOkFERERUH6WlpWmsuq3ojk2ZmZlQKpVa+/l6eHjo3M8XKLtn/LvvvouUlJRK7xRVGYPPev3111FaWoqZM2eioKAAw4YNQ8OGDbFs2TIMGTKkSp0gIiIiqi3G3DBZJpMZtL3K4/eAr+i+8EqlEsOGDcOHH36IgAD991h8XJXCw3HjxmHcuHHIzMyESqWqsVtoERERERmdMbZHMfB8V1dXWFhYaGXvMjIydN7pKy8vD8eOHcPJkyfx1ltvAQBUKhUEQYClpSV++ukn9OjR44nXrdadMVxdeQslIiIioieRSCQICgpCcnIyXn75ZXV5cnIy+vfvr1VfJpNp7WYSFxeHX3/9FVu2bIGfn59e1zU40PPz89OZYix39epVQ5skIiIiqj1GGLqtSkZw6tSpGDlyJIKDgxESEoLVq1cjNTUVEydOBADMnj0bt27dwoYNGyAWi9G6dWuN893d3SGVSrXKK2NwoBcZGanxuKSkBCdPnsSePXswY8YMQ5sjIiIiql11MHQLAIMHD0ZWVhaio6ORnp6O1q1bY/fu3fDx8QEApKenP3FPPUMZ7V63n3/+OY4dO4Z169YZozkig/Fet+aF97o1L7zXrXmorXvd+r8fA4tq3utWWViIq/PfM8173erSt29fbN261VjNEREREdUMM7rXbbUWYzxqy5YtcHZ2NlZzRERERDXCmNur/NsZHOh16NBBYzGGIAi4c+cO7t27h7i4OKN2joiIiIiqzuBAb8CAARqPxWIx3Nzc0K1bNwQGBhqrX0RERERUTQYFeqWlpfD19UXv3r3h6elZU30iIiIiqjl1tOq2Lhi0GMPS0hJvvvkmioqKaqo/RERERDWqfI5edY/6wOBVt506dcLJkydroi9EREREZEQGz9GLiIjAtGnTcPPmTQQFBcHOzk7j+bZt2xqtc0REREQ1op5k5KpL70DvjTfewNKlSzF48GAAwJQpU9TPiUQiCIIAkUgEpVJp/F4SERERGYsZzdHTO9Bbv349PvnkE1y7dq0m+0NERERERqJ3oFd+p7Ty+7ERERER1UfcMLkCj26UTERERFQvcehWt4CAgCcGe/fv369Wh4iIiIjIOAwK9D788EPI5fKa6gsRERFRjePQbQWGDBkCd3f3muoLERERUc0zo6FbvTdM5vw8IiIiovrF4FW3RERERPWaGWX09A70VCpVTfaDiIiIqFZwjh4RERGRqTKjjJ7ec/SIiIiIqH5hRo+IiIjMixll9BjoERERkVkxpzl6HLolIiIiMlHM6BEREZF54dAtERERkWni0C0RERER1XvM6BEREZF54dAtERERkYkyo0CPQ7dEREREJooZPSIiIjIrov8/qttGfcBAj4iIiMyLGQ3dMtAjIiIis8LtVYiIiIio3mNGj4iIiMwLh26JiIiITFg9CdSqi0O3RERERCaKGT0iIiIyK1yMQURERGSqBCMdVRAXFwc/Pz9IpVIEBQUhJSWlwrqHDh1CWFgYXFxcYGNjg8DAQCxZssSg6zGjR0RERFQLkpKSEBkZibi4OISFhWHVqlXo27cvzp07h8aNG2vVt7Ozw1tvvYW2bdvCzs4Ohw4dwoQJE2BnZ4fx48frdU1m9IiIiMislA/dVvcw1OLFizFmzBiMHTsWLVq0wNKlS+Ht7Y34+Hid9Tt06IChQ4eiVatW8PX1xYgRI9C7d+9Ks4CPY6BHRERE5sWIQ7cKhULjKCoq0nnJ4uJiHD9+HOHh4Rrl4eHhOHz4sF7dPnnyJA4fPoxnn31W75fKQI+IiIioiry9vSGXy9VHbGysznqZmZlQKpXw8PDQKPfw8MCdO3cqvUajRo1gbW2N4OBgTJo0CWPHjtW7f5yjRyanVC4BLK3ruhtUwzrMj6jrLlAterCxoK67QLVAVVAIjNle49cx5qrbtLQ0yGQydbm1deW/f0QikcZjQRC0yh6XkpKCBw8e4MiRI3j33XfRtGlTDB06VK9+MtAjIiIi82LEO2PIZDKNQK8irq6usLCw0MreZWRkaGX5Hufn5wcAaNOmDe7evYt58+bpHehx6JaIiIjMSx1sryKRSBAUFITk5GSN8uTkZISGhurfdUGocB6gLszoEREREdWCqVOnYuTIkQgODkZISAhWr16N1NRUTJw4EQAwe/Zs3Lp1Cxs2bAAAfP7552jcuDECAwMBlO2rt2jRIkyePFnvazLQIyIiIrNSV3fGGDx4MLKyshAdHY309HS0bt0au3fvho+PDwAgPT0dqamp6voqlQqzZ8/GtWvXYGlpiSZNmuCTTz7BhAkT9L4mAz0iIiIyL0aco2eoiIgIREToXkyWmJio8Xjy5MkGZe904Rw9IiIiIhPFjB4RERGZFZEgQCRUL6VX3fNrCwM9IiIiMi91OHRb2zh0S0RERGSimNEjIiIis1JXq27rAgM9IiIiMi8cuiUiIiKi+o4ZPSIiIjIrHLolIiIiMlVmNHTLQI+IiIjMijll9DhHj4iIiMhEMaNHRERE5oVDt0RERESmq74MvVYXh26JiIiITBQzekRERGReBKHsqG4b9QADPSIiIjIrXHVLRERERPUeM3pERERkXrjqloiIiMg0iVRlR3XbqA84dEtERERkopjRIyIiIvPCoVsiIiIi02ROq24Z6BEREZF5MaN99DhHj4iIiMhEMaNHREREZoVDt0RERESmyowWY3DoloiIiMhEMaNHREREZoVDt0RERESmiqtuiYiIiKi+Y0aPiIiIzAqHbomIiIhMFVfdEhEREVF9x4weERERmRUO3RIRERGZKpVQdlS3jXqAgR4RERGZF87RIyIiIqL6jhk9IiIiMisiGGGOnlF6UvOY0SMiIiLzUn5njOoeVRAXFwc/Pz9IpVIEBQUhJSWlwrrbtm1Dr1694ObmBplMhpCQEOzdu9eg6zHQIyIiIqoFSUlJiIyMRFRUFE6ePIkuXbqgb9++SE1N1Vn/4MGD6NWrF3bv3o3jx4+je/fueOmll3Dy5Em9r8mhWyIiIjIrxtxeRaFQaJRbW1vD2tpa5zmLFy/GmDFjMHbsWADA0qVLsXfvXsTHxyM2Nlar/tKlSzUex8TEYPv27di5cyc6dOigVz+Z0SMiIiLzIhjpAODt7Q25XK4+dAVsAFBcXIzjx48jPDxcozw8PByHDx/Wq9sqlQp5eXlwdnbW+6Uyo0dERERURWlpaZDJZOrHFWXzMjMzoVQq4eHhoVHu4eGBO3fu6HWtzz77DPn5+Xj11Vf17h8DPSIiIjIrIkGAqIqLKR5tAwBkMplGoPfE80Sa63UFQdAq02XTpk2YN28etm/fDnd3d72vx0CPiIiIzIvq/4/qtmEAV1dXWFhYaGXvMjIytLJ8j0tKSsKYMWPw7bffomfPngZdl3P0iIiIiGqYRCJBUFAQkpOTNcqTk5MRGhpa4XmbNm3C6NGjsXHjRrzwwgsGX5cZPSIiIjIrxhy6NcTUqVMxcuRIBAcHIyQkBKtXr0ZqaiomTpwIAJg9ezZu3bqFDRs2ACgL8kaNGoVly5ahc+fO6mygjY0N5HK5XtdkoEdERETmpY7udTt48GBkZWUhOjoa6enpaN26NXbv3g0fHx8AQHp6usaeeqtWrUJpaSkmTZqESZMmqctfe+01JCYm6nVNBnpERERkXqpxZwuNNqogIiICEREROp97PHjbv39/la7xKM7RIyIiIjJRzOgRERGRWTHmnTH+7RjoEf3L9Ot5HoNeOAMXx4e4fssRcV8+jTMXPXXWdXYswMThf6KZbyYaeirw3d6WiP+qk0YdCwsVhvb7H8K7/A1XpwKkpcvwxeZg/Pm/RrXxcqgSg4LO4LWQU3C1L8CVe05Y9FMYTqY10Fm3vXc63u5xBL4u2ZBalSI91wFbT7TE13+0U9fxd72PiGf/QAuvTDRwzMOnP4Vi4yPP07+LLDkT8l0ZsMgpQUlDKbJGNURhoL3OutJzeWgw/4pWedqngShpKK3prpqeOhy6rW0cutVBJBJVeowePVpdd9euXejWrRscHBxga2uLjh07aoyxnz59GtbW1tixY4fGNbZu3QqpVIozZ84AAObNm4f27dtr1FEoFIiKikJgYCCkUik8PT3Rs2dPbNu2DUIFX2BKpRKxsbEIDAyEjY0NnJ2d0blzZ6xbt05dZ/To0erXYmVlBX9/f0yfPh35+fkAgOvXr1f42o8cOQKgbB6BruelUs0fOHfu3MHkyZPh7+8Pa2treHt746WXXsIvv/yiruPr66t1P7+K3hNT163zVbw58g9s3N4OE6P64a8LHoidmQx3lwc661tZKpGjsMbG7e1wNVX3LXFeH3QcL/a4iP+u74QxM1/Grl8CMe+dX9HUJ6smXwo9QXjLvzEj/DckHHoKQ9cMwslUL/x36A/wlOXprP+w2BJJx1pjzIYB+M/KIfjiUBAmdfsD/+lwTl1HalWKmzkyLP+1E+7l2dbWS6EqsPs9Gy4bbiFngAduxTRHYaAdPBdchUVmcaXnpX0WiBtxrdRHiZfuuzAQlWNGT4f09HT1v5OSkjBnzhxcvHhRXWZjYwMAWLFiBSIjIzFr1izExcVBIpFg+/btmDhxIs6cOYNFixahXbt2+OCDDzB+/HiEhYXBxcUFGRkZmDhxIj788EO0bt1aZx9ycnLwzDPPIDc3F/Pnz0fHjh1haWmJAwcOYObMmejRowccHR21zps3bx5Wr16N//73vwgODoZCocCxY8eQnZ2tUa9Pnz5Yt24dSkpKkJKSgrFjxyI/Px/x8fHqOj///DNatWqlcZ6Li4v63zKZTON9ATR3/L5+/TrCwsLg6OiIhQsXom3btigpKcHevXsxadIkXLhwoaKPwGwN7HsWe/Y3w4/7AwAA8V91QnDbW3ip5wUkJAVr1b+b6YC4LzsDAPo8e1lnmz2fuYKN29vhj9PeAICdvwQiuO0tvPL8GXwS/2wNvRJ6khGdTuP7U4H47lRLAMCi5GcQ0iQNg4LOYsW+zlr1L951w8W7burH6bky9Ai8ig6N07HtZFkb59LdcS69bMf8KT2O1sKroKqS776HvG7OyOte9jM1a1Qj2PwvD7KfM5E9RHdWFwCUMkuo7Piru7pEqrKjum3UB/xq0cHT859hMrlcDpFIpFEGlN3bbtq0aYiMjERMTIy6fNq0aZBIJJgyZQoGDRqETp06Yfbs2dixYwcmTZqEzZs3Y8KECWjWrBmmT59eYR/ee+89XL9+HZcuXUKDBv980wcEBGDo0KFambNyO3fuREREBAYNGqQua9dOe+jG2tpa/ZqGDRuGffv24fvvv9cI9FxcXLRe96N0vS+PioiIgEgkwh9//AE7Ozt1eatWrfDGG29UeJ65srRQIsAvC5t3ttUoP/5XA7RsllHldiWWKhQXW2iUFRVboHXzqrdJ1WMpVqKF1z2sO9xBo/zIVW+0a6TfPS+be9xDu0Z3ELf/6ZroItWkUhWsrxUgp5/mbawetnGA9FJ+pac2fO8SRCUqlDSUInuABwpbOdRkT00Xh27pSbZs2YKSkhKdwdqECRNgb2+PTZs2AQAsLCywfv16bN++HcOGDcPevXuRmJgICwsLrXMBQKVSYfPmzRg+fLhGkFfO3t4elpa6Y3RPT0/8+uuvuHfvnkGvx8bGBiUlJQadU5n79+9jz549mDRpkkaQV05XNtJQRUVFUCgUGkd9JncogoWFgOxczSA+O9cGzvKHVW732F8N8crzZ9HQIxcikYCnWt9CaFAqnB0LqttlqiIn20JYigXcz9ccXs3Kt4GLfeWfy54pG3D03VX4esxWfHOstTojSPWHRZ4SIhWglFtplCvlVrDILdV5jtLRCvfGeuNupC/uRvqh2MsaXjFXID2ve1oHUTlm9Kro0qVLkMvl8PLy0npOIpHA398fly5dUpe1aNECkZGR+OSTT7BgwQIEBARU2HZmZiays7MRGBhocL8WL16MV155BZ6enmjVqhVCQ0PRv39/9O3bt8Jz/vjjD2zcuBHPPfecRnloaCjEYs2/BXJzc9UBam5uLuzt7bXO+emnn/D3339DEAS9X8OsWbPw/vvva5QVFxejZcuKf4nFxsbiww8/1Kv9+kQQNG9uLRIBAp58w+uKfL6hE6aO/Q1rF30HCMDtuw7Ye7AZenfVPdRLtefxhIAI2p//497YMAC2ViVo0+gupnQ/grRsOfacbVZznaRaVHGGqKSBFCUN/vkjsCjADpZZJZD/kIHCFroXcFAl6mjD5LrAQK+GCIKgMV/twYMHSEpKgq2tLVJSUjBz5sxKzwU057vpq2XLljhz5gyOHz+OQ4cO4eDBg3jppZcwevRofPHFF+p6u3btgr29PUpLS1FSUoL+/ftjxYoVGm0lJSWhRYsWGmWPZiEdHBxw4sQJjefL5y8a+hpmzJihscgFAJYvX46DBw9WeM7s2bMxdepU9WOFQgFvb2+9rvdvlJtnDaVSBGdHzeydo+yhVpbPsHalmLvkOVhZlUJmX4SsbFuMHXIMd+5xyKeuZBdIUaoSaWXvnO0e4n6+TaXn3s6RAQD+vucCF7uHmND1TwZ69YzSwQKCGLDI1RxFscgthVKu/6/loma2sD+U/eSKpKWuboFWFxjoVVFAQAByc3Nx+/ZtreHV4uJiXL16FT169FCXzZgxAxKJBIcPH0ZISAg2bNiAUaNG6Wzbzc0NTk5OOH/+fJX6JhaL0bFjR3Ts2BHvvPMOvvrqK4wcORJRUVHw8/MDAHTv3h3x8fGwsrJCgwYNYGVlpdWOt7c3mjZtWul1Knq+WbNmEIlEOH/+PAYMGPDEPru6umq15eysexVpOWtra1hbm86Ks1KlBS5dc0FQ69v47ZiPujyozW0cPt642u2XlFgiK9sSFhYqdOl4AweO+lW7TaqaUpUFzqe7obPfTey76K8u7+x3E/sv+erdjggCJBb1ZEY4/cNSjCI/W9j8lYeCjo7qYpszecgP0u/+pQAguf4QSkftn91Ej+IcvSoaOHAgLC0t8dlnn2k9t3LlSuTn52Po0KEAgOTkZHzxxRdITExEu3btEBMTg8jISI3VvY8Si8UYPHgwvv76a9y+fVvr+fz8fJSW6p7HoUv58Gf59ikAYGdnh6ZNm8LHx0dnkFddzs7O6N27Nz7//HON65bLyckx+jVNwdYfW6Fv90vo8+wlNG6QgzdHHIW7Sz52/lI2BD5m8DHMmqiZ5Wzik4UmPlmQSkvgKCtEE58sNG6Yo34+sMk9PBN8HV5ueWjd/A5iZ/4EsVhA0i7dK76pdnx1tB1e7nAe/dudh59LNqb1+g2e8jxsOVG20n1y9yP4qN8/2xC9GnQGXZtdR2OnHDR2ykG/dhcwsvNp/HDmn2yepViJAI9MBHhkwspCCXeHfAR4ZMLbKbfWXx9VLvd5N8j23YfD/ixY3SqEy5e3YJlZgrznXAEATptvwy3uhrq+7McM2P6ZA8v0IljdfAinzbdh/0cucsNd6+ol1G/lizGqe9QDzOhVUePGjbFw4UJMnz4dUqkUI0eOhJWVFbZv34733nsP06ZNQ6dOnaBQKDBmzBhMnz4dnTuXbZkwZcoUbN26FePHj8fOnTt1th8TE4P9+/ejU6dO+PjjjxEcHAwrKyukpKQgNjYWf/75p84FDa+88grCwsIQGhoKT09PXLt2DbNnz0ZAQIDBc/6ysrJw547mCkBHR0f1il9BELSeBwB3d3eIxWLExcUhNDQUTz/9NKKjo9G2bVuUlpYiOTkZ8fHxVc5YmrL9R/whsy/CiJdPw9mxANdvOuG9T3shI7NsDo6z40O4u2gGzqti/tmjsbl/Fp4Lu4o79+wxIrJs5bXESonXXz0BL7cHeFhkiT9ONcKC+K7ILzCdbGh99NO5ppDbFGJ8l+Nwtc/H3/ecMXnzC0jPLRtSd7UvgKf8n4n2YpGAyd2PoKFjHkpVYtzMlmHFr53UgSEAuDnkI2nct+rHr4Wcxmshp3HsRgOM+7J/7b04eqL8ECdkPVDCcdsdWOaUoriRFHdm+qPUTQIAsMwpgWXWP3vqiUoFuGy8DYv7JRAkYhQ3kiJ9hj8edpDV1Uuo3wQA1U2G1484j4Fedbzzzjto0qQJFi1ahGXLlkGpVKJVq1aIj4/H66+/DgCIjIyEXC7XWDQgFouxbt06tGvXrsIhXCcnJxw5cgSffPIJ5s+fjxs3bsDJyQlt2rTBp59+Crlcd3q/d+/e2LRpE2JjY5GbmwtPT0/06NED8+bNq3ClbkV69uypVbZp0yYMGTIEQNmcOF2LUdLT0+Hp6Qk/Pz+cOHECH3/8MaZNm4b09HS4ubkhKChIYxsX0rTj5xbY8XMLnc99uqqLVlnP4a9X2t7/LnhizMz/GKVvZFzfHm+Nb4/rzqzO3dlD4/HmY22w+VibSttLz5Whw/w3jdY/qlmKXq5Q9NKdkbs30Ufjce5LHsh9yaM2umUWzGmOnkio6BYLRPWMQqGAXC5Hl65zYGnJWwKZuqwW/IzNyYNnuB2QOVAVFOL6mPnIzc2FTGb8bGX574keHd6FpUX1foaUKgvx68lPaqyvxsKMHhEREZkXAUbYMNkoPalxDPSIiIjIvPDOGERERERU3zGjR0REROZFBVTjhkP/tFEPMNAjIiIis2JOq245dEtERERkopjRIyIiIvNiRosxGOgRERGReTGjQI9Dt0REREQmihk9IiIiMi9mlNFjoEdERETmhdurEBEREZkmbq9CRERERPUeM3pERERkXjhHj4iIiMhEqQRAVM1ATVU/Aj0O3RIRERGZKGb0iIiIyLxw6JaIiIjIVBkh0EP9CPQ4dEtERERkopjRIyIiIvPCoVsiIiIiE6USUO2hV666JSIiIqK6xIweERERmRdBVXZUt416gBk9IiIiMi/lc/Sqe1RBXFwc/Pz8IJVKERQUhJSUlArrpqenY9iwYWjevDnEYjEiIyMNvh4DPSIiIjIvKsE4h4GSkpIQGRmJqKgonDx5El26dEHfvn2Rmpqqs35RURHc3NwQFRWFdu3aVemlMtAjIiIiqgWLFy/GmDFjMHbsWLRo0QJLly6Ft7c34uPjddb39fXFsmXLMGrUKMjl8ipdk4EeERERmRcjDt0qFAqNo6ioSOcli4uLcfz4cYSHh2uUh4eH4/DhwzX2UhnoERERkXkRYIRAr6wpb29vyOVy9REbG6vzkpmZmVAqlfDw8NAo9/DwwJ07d2rspXLVLREREVEVpaWlQSaTqR9bW1tXWl8kEmk8FgRBq8yYGOgRERGReTHinTFkMplGoFcRV1dXWFhYaGXvMjIytLJ8xsShWyIiIjIvKpVxDgNIJBIEBQUhOTlZozw5ORmhoaHGfHUamNEjIiIiqgVTp07FyJEjERwcjJCQEKxevRqpqamYOHEiAGD27Nm4desWNmzYoD7n1KlTAIAHDx7g3r17OHXqFCQSCVq2bKnXNRnoERERkXkx4tCtIQYPHoysrCxER0cjPT0drVu3xu7du+Hj4wOgbIPkx/fU69Chg/rfx48fx8aNG+Hj44Pr16/rdU0GekRERGRe6ijQA4CIiAhERETofC4xMVHHZarXT87RIyIiIjJRzOgRERGReVEJUG+EV602/v0Y6BEREZFZEQQVBMGwVbO62qgPGOgRERGReRGE6mfkqjvHr5Zwjh4RERGRiWJGj4iIiMyLYIQ5evUko8dAj4iIiMyLSgWIqjnHrp7M0ePQLREREZGJYkaPiIiIzAuHbomIiIhMk6BSQajm0G192V6FQ7dEREREJooZPSIiIjIvHLolIiIiMlEqARCZR6DHoVsiIiIiE8WMHhEREZkXQQBQ3X306kdGj4EeERERmRVBJUCo5tCtwECPiIiI6F9IUKH6GT1ur0JEREREdYgZPSIiIjIrHLolIiIiMlVmNHTLQI9MRvlfV6WlRXXcE6oNyuK67gHVJlVBYV13gWqB6mHZz++azpaVoqTa+yWXosQ4nalhIqG+5B6JnuDmzZvw9vau624QEVE1paWloVGjRkZvt7CwEH5+frhz545R2vP09MS1a9cglUqN0l5NYKBHJkOlUuH27dtwcHCASCSq6+7UGoVCAW9vb6SlpUEmk9V1d6gG8bM2H+b6WQuCgLy8PDRo0ABicc2sFy0sLERxsXGGBCQSyb86yAM4dEsmRCwW18hfgPWFTCYzq18I5oyftfkwx89aLpfXaPtSqfRfH5wZE7dXISIiIjJRDPSIiIiITBQDPaJ6ztraGnPnzoW1tXVdd4VqGD9r88HPmoyFizGIiIiITBQzekREREQmioEeERERkYlioEdERERkohjoEREREZkoBnpE/2/06NEYMGCAVvn+/fshEomQk5Oj9Vzz5s0hkUhw69YtjbqVHYmJiZXWq+zWPFu3bkWnTp0gl8vh4OCAVq1aYdq0aernExMTNdry8vLCq6++imvXrqnr+Pr66rzuJ598AgC4fv16hX07cuSIup3i4mIsXLgQ7dq1g62tLVxdXREWFoZ169ahpKSkyu9pZZ/Hli1bIJVKsXDhQgDAvHnzdPYzMDBQfU63bt0QGRmp8VgkEmHz5s0abS9duhS+vr4VvpflR2UbrVb2unx9fbF06VKt8piYGFhYWKjf//K6lX0NdevWrdJ6j7b1uKtXr2Lo0KFo0KABpFIpGjVqhP79++PSpUvqOo+25eDggODgYGzbtk39vL7vu646EydO1OjPvn378Pzzz8PFxQW2trZo2bIlpk2bpvU9pe97+qTvv9GjR6vr7tq1C926dYODgwNsbW3RsWNHJCYmqp8/ffo0rK2tsWPHDo1rbN26FVKpFGfOnFG/H+3bt9eoo1AoEBUVhcDAQEilUnh6eqJnz57Ytm1bhfdxVSqViI2NRWBgIGxsbODs7IzOnTtj3bp16jqjR49WvxYrKyv4+/tj+vTpyM/PB6Df96++X9t37tzB5MmT4e/vD2tra3h7e+Oll17CL7/8UulnUNF7QnWDd8YgqqJDhw6hsLAQgwYNQmJiIqKiohAaGor09HR1nbfffhsKhULjB7VcLsfRo0cBABcvXtTa9d7d3V3n9X7++WcMGTIEMTEx6NevH0QiEc6dO6fxQxco20n/4sWLEAQBFy5cwIQJE9CvXz+cOnUKFhYWAIDo6GiMGzdO4zwHBwet67Vq1UqjzMXFBUBZkNe7d2+cPn0aH330EcLCwiCTyXDkyBEsWrQIHTp0MPoP+S+++AKTJk3C559/jrFjx6rLW7VqhZ9//lmjrqVl5T/apFIp3n//fQwcOBBWVlYV1it/Lx9l7NvrrVu3DjNnzsTatWvx7rvvAgD+/PNPKJVKAMDhw4cxcOBAja8ViUSiPl+fz7JccXExevXqhcDAQGzbtg1eXl64efMmdu/ejdzcXK1+9enTBzk5Ofj0008xaNAgHDp0CCEhIQD0e9/HjRuH6OhojTJbW1v1v1etWoWIiAi89tpr2Lp1K3x9fZGamooNGzbgs88+w+LFiyt/83R49PsvKSkJc+bM0fgMbWxsAAArVqxAZGQkZs2ahbi4OEgkEmzfvh0TJ07EmTNnsGjRIrRr1w4ffPABxo8fj7CwMLi4uCAjIwMTJ07Ehx9+iNatW+vsQ05ODp555hnk5uZi/vz56NixIywtLXHgwAHMnDkTPXr0gKOjo9Z58+bNw+rVq/Hf//4XwcHBUCgUOHbsGLKzszXq9enTR/0HVUpKCsaOHYv8/HzEx8er61T2/Qs8+Wv7+vXrCAsLg6OjIxYuXIi2bduipKQEe/fuxaRJk3DhwoWKPgL6l2GgR1RFCQkJGDZsGJ599llMmjQJ7733HiQSCTw9PdV1bGxsUFRUpFH2KHd3d50/8HXZtWsXnnnmGcyYMUNdFhAQoJX1EolE6ut5eXlh7ty5GDFiBP7++280b94cQFkgUFGfyrm4uFRYZ+nSpTh48CCOHTuGDh06qMv9/f0xaNAgo91HstzChQsxZ84cbNy4EQMHDtR4ztLS8omv5XFDhw7Fzp07sWbNGkRERFRY79H3siYcOHAADx8+RHR0NDZs2ICDBw+ia9eucHNzU9dxdnYGUPHXij6fZblz587h6tWr+PXXX+Hj4wMA8PHxQVhYmFZdR0dHeHp6wtPTEytXrsTmzZuxY8cOdaCnz/tua2tbYZ2bN29iypQpmDJlCpYsWaIu9/X1RdeuXSvN9lbm0evJ5XKdn2FaWhqmTZuGyMhIxMTEqMunTZsGiUSCKVOmYNCgQejUqRNmz56NHTt2YNKkSdi8eTMmTJiAZs2aYfr06RX24b333sP169dx6dIlNGjQQF0eEBCAoUOHVpgV3rlzJyIiIjBo0CB1Wbt27bTqWVtbq1/TsGHDsG/fPnz//fcagV5l37/Ak7+2IyIiIBKJ8Mcff8DOzk5d3qpVK7zxxhsVnkf/Phy6JaqCvLw8fPvttxgxYgR69eqF/Px87N+/v0av6enpibNnz6qHi/RVnsEoH041hq+//ho9e/bUCPLKWVlZafxiqK53330XH330EXbt2qUV5FWVTCbDe++9h+joaPWQV11ISEjA0KFDYWVlhaFDhyIhIaFGr+fm5gaxWIwtW7aoM4b6sLKygqWlpVG/hr799lsUFxdj5syZOp/X9w+gqtiyZQtKSkp0BmsTJkyAvb09Nm3aBACwsLDA+vXrsX37dgwbNgx79+5FYmKiOjv+OJVKhc2bN2P48OEaQV45e3v7CjPOnp6e+PXXX3Hv3j2DXo+NjY1RP5v79+9jz549mDRpks7v5Zr8bMj4GOgRPWLXrl2wt7fXOPr27atVb/PmzWjWrBlatWoFCwsLDBkypEq/pBs1aqRxrfKMmy6TJ09Gx44d0aZNG/j6+mLIkCFYu3YtioqKKjzn5s2b+PTTT9GoUSMEBASoy2fNmqX1Oh8PVENDQ7XqlAcHly9f1piPVRl931NdfvzxRyxYsADbt29Hz549ddb566+/tNp/dGi3IhEREZBKpZUOD+bm5mq1HR4e/sS2H/9c7e3tkZqaqlFHoVBg69atGDFiBABgxIgR2LJlCxQKxRPbf5Q+n2W5hg0bYvny5ZgzZw6cnJzQo0cPfPTRR7h69WqF7RcVFWH+/PlQKBR47rnn1OX6vO9xcXFaddavXw+g7GtIJpPBy8tLr9epz3uqr0uXLkEul+u8tkQigb+/v8acxRYtWiAyMhKbNm3CvHnzNL6XHpeZmYns7Gy9vz8etXjxYty7dw+enp5o27YtJk6ciB9//LHSc/744w9s3LhR47MBKv/+BSr/2v77778hCILer0HX1+CjmVKqWxy6JXpE9+7dNYY/AODo0aPqX8blEhISNMpGjBihHm4y5K/dlJQUjflUlc0ts7Ozww8//IArV65g3759OHLkCKZNm4Zly5bh999/V899Kv8BLggCCgoK8NRTT2Hbtm0a87pmzJihMSkdKAsCHpWUlIQWLVpolJVnMQRB0Huumr7vqS5t27ZFZmYm5syZg44dO+qce9a8eXOtyfIVzVF7lLW1NaKjo/HWW2/hzTff1FnHwcEBJ06c0Cgrz5BW5vHPFYB6AUW5jRs3wt/fXz001759e/j7+2Pz5s0YP378E69RTp/P8lGTJk3CqFGjsG/fPhw9ehTffvstYmJisGPHDvTq1Utdb+jQobCwsMDDhw8hl8uxaNEijQBdn/d9+PDhiIqK0igrn4NqyNcQoN97aiyP9+3BgwdISkqCra0tUlJSKsxClp8LVG0uZ8uWLXHmzBkcP34chw4dwsGDB/HSSy9h9OjR+OKLL9T1yv94Ki0tRUlJCfr3748VK1ZotFXZ9y9Q+de2oa9B19fg8uXLcfDgQb3Op5rFQI/oEXZ2dmjatKlG2c2bNzUenzt3DkePHsWff/6JWbNmqcuVSiU2bdpUYdCgi5+fn8HDIE2aNEGTJk0wduxYREVFISAgAElJSXj99dcB/PMDXCwWw8PDQ+fQi6urq9brfJy3t3eFdQICAnD+/Hm9+qvPe1qRhg0bYuvWrejevTv69OmDPXv2aP2yl0gkT3wtFRkxYgQWLVqE+fPna6y4LScWi6vUtq7P9fEgfu3atTh79qxGuUqlQkJCgkGBnj6f5eMcHBzQr18/9OvXD/Pnz0fv3r0xf/58jUBvyZIl6NmzJ2Qymc4FQvq873K5vNKvodzcXKSnp+uV1dPnPdVX+bVv376tNbxaXFyMq1evokePHuqyGTNmQCKR4PDhwwgJCcGGDRswatQonW27ubnByclJ7++Px4nFYnTs2BEdO3bEO++8g6+++gojR45EVFQU/Pz8APzzx5OVlRUaNGigc0FRZd+/5dep6PlmzZpBJBLh/PnzOlfNP07X12D53FKqexy6JTJQQkICunbtitOnT+PUqVPqY+bMmTU+x+pxvr6+sLW11ZhnVv4D3N/f36hz5R41bNgw/Pzzzzh58qTWc6WlpUad99a4cWMcOHAAGRkZCA8PN3hoszJisRgxMTGIj4/H9evXjdbuk/z11184duwY9u/fr/E1dPDgQfz5558Gz8OsjvJtUR7/zDw9PdG0adMKV4FX1yuvvAKJRKLeKudxVV2MoY+BAwfC0tISn332mdZzK1euRH5+PoYOHQoASE5OxhdffIHExES0a9cOMTExiIyM1Fjd+yixWIzBgwfj66+/xu3bt7Wez8/PR2lpqd59bdmypfq8cuV/PPn4+FS6aryqnJ2d0bt3b3z++ec6v5dr8rMh42NGj8gAJSUl+PLLLxEdHa21tcLYsWOxcOFCnD59WudKOV0yMjJQWFioUebi4qLzh/e8efNQUFCA559/Hj4+PsjJycHy5ctRUlKikYnRR15entZ+fba2thpbvWRlZWnVcXR0hFQqRWRkJH744Qc899xz+Oijj/DMM8/AwcEBx44dw4IFC5CQkGDU7VUaNWqE/fv3o3v37ggPD8fevXshl8sBlAWWj/dTJBLBw8NDr7ZffPFFdOrUCatWrdI6RxAEnfsauru7Qyyu+t/JCQkJePrpp9G1a1et50JCQpCQkKCxErUy+nyW5U6dOoW5c+di5MiRaNmyJSQSCQ4cOIC1a9dqZKf1oc/7XlBQoFXH2toaTk5O8Pb2xpIlS/DWW29BoVBg1KhR8PX1xc2bN7FhwwbY29vrDMSMoXHjxli4cCGmT58OqVSKkSNHwsrKCtu3b8d7772HadOmoVOnTlAoFBgzZgymT5+Ozp07AwCmTJmCrVu3Yvz48di5c6fO9mNiYrB//3506tQJH3/8MYKDg2FlZYWUlBTExsbizz//1JnJf+WVVxAWFobQ0FB4enri2rVrmD17NgICAgye81fZ9y/w5K/tuLg4hIaG4umnn0Z0dDTatm2L0tJSJCcnIz4+vsoZS6oDAhEJgiAIr732mtC/f3+t8n379gkAhOzsbGHLli2CWCwW7ty5o7ONNm3aCJMnT9a7TV3H77//rrPtX3/9VRg4cKDg7e0tSCQSwcPDQ+jTp4+QkpKirrNu3TpBLpdX+jp9fHx0XnfChAmCIAjCtWvXKuzbpk2b1O0UFhYKsbGxQps2bQSpVCo4OzsLYWFhQmJiolBSUqL3e1oRXefevn1baN68udCxY0chOztbmDt3rs5+Wltbq8959tlnhbfffrvCx4IgCIcPHxYACD4+PhrvZUXvQ3p6us4+V/a6fHx8hCVLlghFRUWCi4uLsHDhQp1tfPbZZ4Krq6tQVFSkV5uVfZaPu3fvnjBlyhShdevWgr29veDg4CC0adNGWLRokaBUKtX1AAjfffedzjYEQdD7fddVp3fv3hptJScnC7179xacnJwEqVQqBAYGCtOnTxdu376t93takSd9P2zfvl3o0qWLYGdnJ0ilUiEoKEhYu3at+vnXX39daN26tfqzKHf58mXB1tZWWL9+vfr9aNeunUadnJwc4d133xWaNWum/n7t2bOn8N133wkqlUpnf1avXi10795dcHNzEyQSidC4cWNh9OjRwvXr19V1KvqeKqfP96++X9u3b98WJk2aJPj4+AgSiURo2LCh0K9fP2Hfvn3qOhV9BrreE6obIkGoYItuIiIiIqrXOEePiIiIyEQx0CMiIiIyUQz0iIiIiEwUAz0iIiIiE8VAj4iIiMhEMdAjIiIiMlEM9IiIiIhMFAM9IiIiIhPFQI+IyIjmzZuncfu30aNH63VjeGO7fv06RCIRTp06VWEdX19fLF26VO82ExMTdd66y1AikQjff/99tdshoidjoEdEJm/06NEQiUQQiUSwsrKCv78/pk+frvOG7ca2bNkyJCYm6lVXn+CMiMgQlnXdASKi2tCnTx+sW7cOJSUlSElJwdixY5Gfn4/4+HituiUlJbCysjLKdeVyuVHaISKqCmb0iMgsWFtbw9PTE97e3hg2bBiGDx+uHj4sH25du3Yt/P39YW1tDUEQkJubi/Hjx8Pd3R0ymQw9evTA6dOnNdr95JNP4OHhAQcHB4wZMwaFhYUazz8+dKtSqbBgwQI0bdoU1tbWaNy4MT7++GMAgJ+fHwCgQ4cOEIlE6Natm/q8devWoUWLFpBKpQgMDERcXJzGdf744w906NABUqkUwcHBOHnypMHv0eLFi9GmTRvY2dnB29sbERERePDggVa977//HgEBAZBKpejVqxfS0tI0nt+5cyeCgoIglUrh7++PDz/8EKWlpQb3h4iqj4EeEZklGxsblJSUqB///fff+Oabb7B161b10OkLL7yAO3fuYPfu3Th+/DieeuopPPfcc7h//z4A4JtvvsHcuXPx8ccf49ixY/Dy8tIKwB43e/ZsLFiwAB988AHOnTuHjRs3wsPDA0BZsAYAP//8M9LT07Ft2zYAwJo1axAVFYWPP/4Y58+fR0xMDD744AOsX78eAJCfn48XX3wRzZs3x/HjxzFv3jxMnz7d4PdELBZj+fLlOHPmDNavX49ff/0VM2fO1KhTUFCAjz/+GOvXr8dvv/0GhUKBIUOGqJ/fu3cvRowYgSlTpuDcuXNYtWoVEhMT1cEsEdUygYjIxL322mtC//791Y+PHj0quLi4CK+++qogCIIwd+5cwcrKSsjIyFDX+eWXXwSZTCYUFhZqtNWkSRNh1apVgiAIQkhIiDBx4kSN5zt16iS0a9dO57UVCoVgbW0trFmzRmc/r127JgAQTp48qVHu7e0tbNy4UaPso48+EkJCQgRBEIRVq1YJzs7OQn5+vvr5+Ph4nW09ysfHR1iyZEmFz3/zzTeCi4uL+vG6desEAMKRI0fUZefPnxcACEePHhUEQRC6dOkixMTEaLTz5ZdfCl5eXurHAITvvvuuwusSkfFwjh4RmYVdu3bB3t4epaWlKCkpQf/+/bFixQr18z4+PnBzc1M/Pn78OB48eAAXFxeNdh4+fIgrV64AAM6fP4+JEydqPB8SEoJ9+/bp7MP58+dRVFSE5557Tu9+37t3D2lpaRgzZgzGjRunLi8tLVXP/zt//jzatWsHW1tbjX4Yat++fYiJicG5c+egUChQWlqKwsJC5Ofnw87ODgBgaWmJ4OBg9TmBgYFwdHTE+fPn8fTTT+P48eP4888/NTJ4SqUShYWFKCgo0OgjEdU8BnpEZBa6d++O+Ph4WFlZoUGDBlqLLcoDmXIqlQpeXl7Yv3+/VltV3WLExsbG4HNUKhWAsuHbTp06aTxnYWEBABAEoUr9edSNGzfw/PPPY+LEifjoo4/g7OyMQ4cOYcyYMRpD3EDZ9iiPKy9TqVT48MMP8Z///EerjlQqrXY/icgwDPSIyCzY2dmhadOmetd/6qmncOfOHVhaWsLX11dnnRYtWuDIkSMYNWqUuuzIkSMVttmsWTPY2Njgl19+wdixY7Wel0gkAMoyYOU8PDzQsGFDXL16FcOHD9fZbsuWLfHll1/i4cOH6mCysn7ocuzYMZSWluKzzz6DWFw2ffubb77RqldaWopjx47h6aefBgBcvHgROTk5CAwMBFD2vl28eNGg95qIag4DPSIiHXr27ImQkBAMGDAACxYsQPPmzXH79m3s3r0bAwYMQHBwMN5++2289tprCA4OxjPPPIOvv/4aZ8+ehb+/v842pVIpZs2ahZkzZ0IikSAsLAz37t3D2bNnMWbMGLi7u8PGxgZ79uxBo0aNIJVKIZfLMW/ePEyZMgUymQx9+/ZFUVERjh07huzsbEydOhXDhg1DVFQUxowZg/fffx/Xr1/HokWLDHq9TZo0QWlpKVasWIGXXnoJv/32G1auXKlVz8rKCpMnT8by5cthZWWFt956C507d1YHfnPmzMGLL74Ib29vDBo0CGKxGP/73//w119/Yf78+YZ/EERULVx1S0Skg0gkwu7du9G1a1e88cYbCAgIwJAhQ3D9+nX1KtnBgwdjzpw5mDVrFoKCgnDjxg28+eablbb7wQcfYNq0aZgzZw5atGiBwYMHIyMjA0DZ/Lfly5dj1apVaNCgAfr37w8AGDt2LL744gskJiaiTZs2ePbZZ5GYmKjejsXe3h47d+7EuXPn0KFDB0RFRWHBggUGvd727dtj8eLFWLBgAVq3bo2vv/4asbGxWvVsbW0xa9YsDBs2DCEhIbCxscHmzZvVz/fu3Ru7du1CcnIyOnbsiM6dO2Px4sXw8fExqD9EZBwiwRiTO4iIiIjoX4cZPSIiIiITxUCPiIiIyEQx0CMiIiIyUQz0iIiIiEwUAz0iIiIiE8VAj4iIiMhEMdAjIiIiMlEM9IiIiIhMFAM9IiIiIhPFQI+IiIjIRDHQIyIiIjJR/weuTTlIe7QD4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_2_t_1_trn_100>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_2__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_2__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:23<00:00,  1.87it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.6691449814126393,\n",
      "                 'precision': 0.72,\n",
      "                 'recall': 0.625,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7958477508650518,\n",
      "                       'precision': 0.7142857142857143,\n",
      "                       'recall': 0.8984375,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.4539007092198582,\n",
      "                  'precision': 0.6808510638297872,\n",
      "                  'recall': 0.3404255319148936,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.7125506072874493,\n",
      " 'macro avg': {'f1-score': 0.6396311471658497,\n",
      "               'precision': 0.7050455927051672,\n",
      "               'recall': 0.6212876773049646,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.6938473040719434,\n",
      "                  'precision': 0.7095893580104106,\n",
      "                  'recall': 0.7125506072874493,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_2_t_1_trn_100>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>▇▅▆▆▅▅█▇▅▃▁▂▃▄▁▃</td></tr><tr><td>train_mean_token_accuracy</td><td>▃▃▃▃▃▄▁▂▅▇▇▅▆▅█▅</td></tr><tr><td>valid_loss</td><td>▆▃▅▅▅█▆▂▂▃▆▄▅▁▄▆</td></tr><tr><td>valid_mean_token_accuracy</td><td>▄▇▄▅▅▁▃▇▆▄▃▅▆█▄▄</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.71255</td></tr><tr><td>f1</td><td>0.71255</td></tr><tr><td>precision</td><td>0.71255</td></tr><tr><td>recall</td><td>0.71255</td></tr><tr><td>train_loss</td><td>0.05983</td></tr><tr><td>train_mean_token_accuracy</td><td>0.97297</td></tr><tr><td>valid_loss</td><td>0.09444</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.96053</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_alliance</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gedg67p4</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_105836-gedg67p4\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chatGPT",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
